Are there relatively new research papers that describe how to make back-propagation more efficient?
Here is a paper that explains why ReLU rules.
What we want is to disentangle data of different classes. In order to do that, we need a discontinuous mapping for the data. ReLU easily allows for that. It is even better than LeakyReLU, sigmoid and tanh in that regard. Also, the reason any of the activations work is because of the floating point error, there is inadvertently a discontinuous mapping for the whole data. I have also explained it here.